Biology subjects

Dang, T.

Publications and source records attributed to Dang, T..

At least 19 recordsLinked to original sources

bioRxiv2026

QBayMic: Quantum-coupled variational Bayes for clustering and feature selection in low-signal microbiome data

Clustering microbiome samples into community types is central to cohort stratification and biomarker discovery, yet the resulting inference becomes unstable when the between group signal is small compared with sampling noise: variational Bayes yields different partitions across initialisations, and common fixes do not solve the problem. Simple restarts are ineffective because the variational free energy is anti-correlated with clustering accuracy; deterministic annealing collapses to the same solution as greedy ascent, with the operator staying diagonal at every temperature; and parallel tempering replicas remain too similar to permit configuration exchanges. We propose QBayMic, which replaces the assignment step of a Dirichlet-multinomial mixture with sparse variable selection via a quantum Gibbs state under an annealed Hamiltonian, coupling competing assignments through a transverse-field term that cannot be reproduced by temperature scaling alone. We present two gate-based circuit designs for this step, evaluating on noiseless qubit-register simulations, and we derive a signal fraction, computable prior to clustering, that predicts the expected strength of quantum coupling. With matched compute in the predicted regime, the three classical methods recovered the reference partition (ARI > 0.4) in 0/100 seeds, while QBayMic recovered it in 47-64/100; when the number of clusters exceeded three, only QBayMic recovered the correct cluster count. For a soil pH dataset, the diagnostic indicates a narrow separation margin; for a human-derived dataset tuned into the predicted band via controlled dilution, classical methods recovered the cluster count in 0/100 seeds, compared with 61-76% for QBayMic. The implementation is publicly available at https://github.com/tungtokyo1108/QBayMic.

bioinformatics↗

bioRxiv2026

A hyperspherical deep Bayesian model for interpretable clustering and relationship prediction in microbiome multi-omics integration

The microbiome plays a significant role in the development and progression of many diseases, yet extracting interpretable insights from multi-omics data remains challenging. Existing approaches face a recurring practical trade-off: deep learning methods achieve high predictive performance but lack uncertainty quantification, whereas probabilistic methods provide interpretable results but require data-type-specific likelihood functions that limit generalization across diverse omics modalities. Here, we introduce DBayesCM (Deep Bayesian Clustering for Multi-omics), which combines deep learning modeling with Bayesian nonparametric methods. DBayesCM employs separate encoders to project microbiome and host omics data into a shared latent space, where an infinite mixture model with a Dirichlet process prior determines the number of clusters automatically while quantifying the uncertainty of each samples assignment. Spike-and-slab priors identify discriminative features, and a Bayesian neural network estimates probabilistic co-occurrence between microbial species and host omics features. To isolate the effect of latent geometry, we evaluate two variants that are identical except for their latent space: DBayesCM-vMF constrains the latent to the unit hypersphere and applies a von Mises-Fisher mixture, while DBayesCM-GMM uses a Euclidean latent space and a Gaussian mixture. On simulated data, the hyperspherical variant recovered the correct number of clusters, whereas the Euclidean variant over-segmented, demonstrating that the latent geometry affects cluster recovery. Applied to colon, breast, and kidney cancer cohorts spanning metagenomics, host metabolomics, RNA-seq, and miRNA data, and to an obstructive sleep apnea model, DBayesCM ranked consistently among the existing methods while uniquely combining data-driven cluster-number determination, sample-level uncertainty, and interpretable feature selection within a single framework. DBayesCM reveals conditional probabilistic co-occurrence between core microbial species and host omics features, enabling uncertainty-aware exploration of microbiome-host relationships across diverse diseases.

bioinformatics↗

bioRxiv2026

Mechanical Tension Actively Triggers RhoA-Mediated Cell Extrusion

Cell extrusion is a fundamental process in tissue homeostasis, morphogenesis, and cancer progression, facilitating the removal of cells either alive or through apoptosis. While biochemical signaling pathways are known to regulate extrusion, recent advances have underscored the importance of mechanical forces in this process. Here, using optogenetic control of RhoA activation in epithelial monolayers combined with Bayesian Inversion Stress Microscopy (BISM) and three-dimensional cell-based modeling, we uncover a counterintuitive mechanism whereby elevated tension, instead of stabilizing the monolayer, actively drives extrusion in highly contractile cells. We show that local RhoA activation enhances myosin II-dependent contractility and F-actin reorganization, which promotes cell stiffening, resulting in localized tension buildup. The ensuing tensile stress amplifies vertical mechanical fluctuations, which in turn trigger cell extrusion. Remarkably, these tension-induced extrusions occur both apically and basally. Furthermore, our findings show that RhoA-mediated contractility is not merely an effector of extrusion but also an active promoter of basal extrusion, independently of caspase activation. Our study demonstrates that tensile stress can directly initiate extrusion events and bias their outcome toward apical or basal fates. By identifying tension as a driver rather than a suppressor of extrusion, this work revises current models of epithelial homeostasis and highlights mechanical control as a targetable axis in disease and regeneration.

cell biology↗

bioRxiv2026

Multi omics reveals mesodermal fate bias and enables predictive cell state control in human pluripotent stem cell biomanufacturing

Human induced pluripotent stem cells can retain conventional markers of pluripotency while acquiring environmentally dependent molecular states that may influence their developmental competence. Yet, how biomanufacturing conditions reorganize the interconnected networks governing cell state remains poorly characterized. Here, we first examined compensation of hiPSCs to static aggregation and dynamic agitation by proteomics. We then examined hiPSCs during single-passage culture in stirred-suspension bioprocesses under varying oxygen and agitation conditions. Intracellular metabolomic and transcriptomic profiling revealed distinct responses across culture configuration, hydrodynamic exposure, and oxygenation. Transition into dynamic culture broadly remodeled mitochondrial organization, carbon allocation, mechanotransduction, proteostasis, and developmental regulation. Increasing agitation produced a persistent response involving cellular architecture, growth-factor signaling, genome maintenance, and Epiblast- and lineage-aligned programs, whereas oxygenation elicited a smaller and more transient metabolic and transcriptional response. Through comparison with a human gastrulation reference, we show that these adaptations intersect with natural developmental programs without reproducing coherent epiblast state or lineage commitment. These findings demonstrate that bioprocess conditions can preserve core pluripotent identity while remodeling broader molecular states associated with developmental responsiveness, providing perspective on evaluating and optimizing hiPSC quality in biomanufacturing beyond restricted marker panels.

bioengineering↗

bioRxiv2026

Novel mouse reporter models for the detection of genome editing events in vivo

With the expansion of therapeutic gene editing technology, small animal models provide essential platforms to evaluate the function of these new approaches in vivo. As part of the Somatic Cell Genome Editing (SCGE) Consortium, we developed next-generation murine reporters that overcome current model limitations and broaden detectable in vivo editing outcomes. These include two mouse models built on the "traffic light" reporter concept. This system enables fluorescent detection of both gene repair (green) and CRISPR-generated indels (red) events following editing by a single guide and either dsDNA or single-stranded oligonucleotide donor. We also generated a third reporter model that efficiently detects A-base editor activity. Reporters were validated in cultured embryos, via germline editing, and through activation in vivo by AAV transduction or direct ribonucleoprotein delivery. Together, these new models provide a valuable resource for improved detection of genome editing events in vivo.

genomics↗

bioRxiv2026

EpCAM sustains crypt mechanics and prevents aberrant remodeling

Crypt morphogenesis is a fundamental process that drives intestinal growth and maintains its homeostasis. Although intestinal tissue mechanics is recognized as pivotal for early crypt initiation and budding, the molecular mechanisms that couple epithelial forces to mature crypt behavior remain largely unexplored. In particular, while Paneth cells have been proposed to be implicated in the mechanical control of crypt remodeling and fission, the underlying pathways have remained elusive. Here, we identified EpCAM (Epithelial Cell Adhesion Molecule) as a key regulator of epithelial contractility and crypt morphogenesis. EpCAM was preferentially enriched at lateral interfaces of Paneth cells in vivo and in organoid models, where it maintained cortical tension and contractile organization. Loss of EpCAM disrupted crypt cell cortical myosin II-A localization, leading to aberrant Paneth cell apical contractility, enlarged apical surfaces, distorted pyramidal morphology, and altered aspect ratios. Consequently, Paneth cells lose their tight clustering in crypt base, their spatial distribution along the crypt axis becomes irregular, and the collective architecture of the crypt epithelium was compromised. At the tissue level, EpCAM deficiency profoundly perturbed crypt fission, increasing fission frequency while favoring asymmetric outcome both in EPCAM-mutated Congenital Tufting Enteropathy (CTE) patients. Live imaging of EpCAM-knockout mouse organoids confirmed a failure in the spatial control of crypt bifurcation. Collectively, our findings revealed that EpCAM is not required to initiate fission, but is essential to coordinate the mechanical forces and ensure correct division of the crypt. We showed that EpCAM-dependent biomechanical integrity of Paneth cells generates a stabilizing force field at the crypt base, orchestrating coordinated tissue remodeling. Disruption of this program led to unbalanced force transmission, distorted crypt geometry, and aberrant fission outcomes, revealing a critical link between epithelial mechanics and intestinal morphogenesis.

developmental biology↗

bioRxiv2026

Vimentin bridges scales to convert polarized cell locomotion into coordinated collective migration

Collective cell migration is central in development and disease. Vimentin is an intermediate filament protein expressed by epithelial cells at the edge of wounds where collective cell migration is most efficient. Yet, its functional role in this context remains underexplored. Here, we show that vimentin, over-expressed in cells undergoing partial epithelial to mesenchymal transition at the edge of epithelial monolayers, has a multiscale impact on the whole monolayer mechano-dynamics. Vimentin knock-down delays wound closure, reduces cell coordination, while increasing traction forces exerted by cells on the substratum. It also disrupts the directionality of leader cells migration, as well as the cohesion and coordinated motion of cells deep in the monolayer. We further show that vimentin promotes the conversion of polarized cell locomotion into coordinate collective migration by polarizing actin, focal adhesions and traction forces, sustaining leader cells lamellipodium protrusive activity and directionality, while allowing mechanical coupling of leader with follower cells. Altogether, we show that vimentin is essential for bridging polarized single cell locomotion and coordinated collective migration to allow efficient collective migration. Significant statementEpithelial cells migrate coordinately to repair tissue. Vimentin, transiently enriched at the wound edge plays a key role in guiding this process by converting polarized cell locomotion into coordinate collective migration, enabling leader cells to polarize, form stable protrusions, and maintain directed migration, while allowing mechanical coupling of front cells with followers, necessary for efficient coordinated collective migration. These findings reveal vimentin, a known EMT marker, as a novel positive regulator of efficient wound healing.

cell biology↗

bioRxiv2025

Regulation of epithelial tissue homeostasis by active trans-epithelial transport

Epithelia are intricate tissues whose function is intimately linked to mechanics. While mechanobiology has primarily focused on factors such as cell-generated contractility and mechanical properties of extracellular matrix (ECM), a novel mechanobiological paradigm highlights the role of osmotic and mechanical pressures in shaping epithelial tissues. In our study, we developed an in vitro model of cell coated micro-sized hydrogel spheres (MHSs) which allows to decipher the interplay between cellular activities and tissue mechanics. Drastic, isotropic MHS compressions were observed once the epithelia reached confluence. Further studies revealed that the compression was a process independent of cell contractility but rather regulated by active transepithelial fluid flow. Compressive stresses of about 7 kPa are generated by such an active hydraulic mechanism. Tissue homeostasis is then maintained by a fine balance between cell proliferation and extrusion. Our findings demonstrate the critical role of fluid transport in generating mechanical forces within epithelial tissues. Supported by a theoretical mechano-hydraulic model, a mechanistic framework for understanding the intricate interplay between cellular processes and tissue mechanics was established. These results challenge traditional views of epithelial tissue mechanics, emphasizing the pivotal influence of osmotic and mechanical pressures in shaping tissues. We anticipate that this study will advance the understanding of epithelial tissue development, the maintenance of homeostasis, and the mechanisms underlying pathological conditions. Significance StatementEpithelial tissues are vital for many bodily functions, but their mechanics remain poorly understood. Our study uncovers a novel mechanism by which epithelial cells generate mechanical stress, not through traditional cell contractility, but by actively pumping ions and water across their membranes. Using a model of micro-sized hydrogel spheres (MHSs) coated with epithelial cells, we demonstrate that epithelial cells actively transport fluid across the tissue to compress the MHSs. This facilitates the establishment of tissue homeostasis, which is further maintained by a balanced cell proliferation and extrusion rate. Supported by a theoretical model, our findings highlight the overlooked role of fluid transport in tissue mechanics, offering new insights into how epithelial tissues develop, maintain stability, and respond to disease.

cell biology↗

bioRxiv2025

VN1K: a genome graph-based and function-driven multi-omics and phenomics resource for the Vietnamese population

Vietnam, the 16th most populated nation, remains profoundly underrepresented in global genomic databases. Here, we present VN1K, a first-ever comprehensive and well-curated resource of multi-omics data with a wide-range of phenotypic information of 1,011 unrelated Vietnamese individuals. High-depth short-read whole-genome sequencing data were generated for all samples along with various - omic data, including microarray, long-read whole-genome sequencing, and RNA sequencing. Using a high-sensitivity variant detection pipeline, which included a pangenome graph reference and a deep-learning framework, we identified nearly 40 million variants of which 8.5 million are novel with nearly 900 thousand short insertions/deletions and 39 thousand structural variants. Specifically, VN1K featured a first-ever whole-genome methylation profile based on long read sequencing. A genotype imputation panel was also created with the highest accuracy on the Vietnamese population. Variants with significantly different allele frequencies in the Vietnamese population compared to others were found to be functionally significant, especially in genes associated with immune diseases (HLA-B, KIR3DL3, KIR2DL1, KIR2DL4) or drug responses (CYP2C19, CYP2D6, VKORC1, CYP2B6). We were also able to map various loci related to hepatitis B virus infection as well as six disease traits, including triglyceride levels, LDL-C, serum glucose levels, HbA1c, and levels of two liver enzymes (ALT and AST). VN1K dataset is accessible via genome.vinbigdata.org, an integrated platform with both linear and graph-based genome browser for facilitating data exploration, research, and applications in precision medicine.

genomics↗

bioRxiv2024

VBayesMM: Variational Bayesian neural network to prioritize important relationships of high-dimensional microbiome multiomics data

The analysis of high-dimensional microbiome multiomics datasets is crucial for understanding the complex interactions between microbial communities and host physiological states across health and disease conditions. Despite their importance, current methods such as the microbe-metabolite vectors (MMvec) approach, often fail to efficiently identify keystone species. This arises from the vast dimensionality of metagenomics data which complicates the inference of significant relationships, particularly the estimation of co-occurrence probabilities between microbes and metabolites. Here we propose the variational Bayesian microbiome multiomics (VBayesMM) approach, which enhances MMvec by incorporating a spike-and-slab prior within a Bayesian neural network. This allows VBayesMM to rapidly and precisely identify crucial microbial species, improving the accuracy of estimated co-occurrence probabilities between microbes and metabolites, while also robustly managing the uncertainty inherent in high-dimensional data. Moreover, we have implemented variational inference to address computational bottlenecks, enabling scalable analysis across extensive multiomics datasets. Our comparative evaluation of large-scale human and mouse multiomics datasets demonstrates that VBayesMM not only outperforms existing methods in accuracy but also provides a scalable solution for analyzing massive datasets. VBayesMM enhances the interpretability of the Bayesian neural network by identifying a core set of influential microbial species, thus facilitating a deeper understanding of their probabilistic relationships with the host.

bioinformatics↗

bioRxiv2024

Paenilamicins from the honey bee pathogen Paenibacillus larvae are context-specific translocation inhibitors of protein synthesis

The paenilamicins are a group of hybrid non-ribosomal peptide-polyketide compounds produced by the honey bee pathogen Paenibacillus larvae that display activity against Gram-positive pathogens, such as Staphylococcus aureus. While paenilamicins have been shown to inhibit protein synthesis, their mechanism of action has remained unclear. Here, we have determined structures of the paenilamicin PamB2 stalled ribosomes, revealing a unique binding site on the small 30S subunit located between the A- and P-site tRNAs. In addition to providing a precise description of interactions of PamB2 with the ribosome, the structures also rationalize the resistance mechanisms utilized by P. larvae. We could further demonstrate that PamB2 interferes with the translocation of mRNA and tRNAs through the ribosome during translation elongation, and that this inhibitory activity is influenced by the presence of modifications at position 37 of the A-site tRNA. Collectively, our study defines the paenilamicins as a new class of context-specific translocation inhibitors.

biochemistry↗

bioRxiv2024

A split-GAL4 driver line resource for Drosophila CNS cell types

Techniques that enable precise manipulations of subsets of neurons in the fly central nervous system have greatly facilitated our understanding of the neural basis of behavior. Split-GAL4 driver lines allow specific targeting of cell types in Drosophila melanogaster and other species. We describe here a collection of 3060 lines targeting a range of cell types in the adult Drosophila central nervous system and 1373 lines characterized in third-instar larvae. These tools enable functional, transcriptomic, and proteomic studies based on precise anatomical targeting. NeuronBridge and other search tools relate light microscopy images of these split-GAL4 lines to connectomes reconstructed from electron microscopy images. The collections are the result of screening over 77,000 split hemidriver combinations. Previously published and new lines are included, all validated for driver expression and curated for optimal cell type specificity across diverse cell types. In addition to images and fly stocks for these well-characterized lines, we make available 300,000 new 3D images of other split-GAL4 lines.

neuroscience↗

bioRxiv2023

An integrative framework of stochastic variational variable selection for joint analysis of multi-omics microbiome data

High-dimensional multi-omics microbiome data plays an important role in elucidating microbial communities interactions with their hosts and environment in critical diseases and ecological changes. Although Bayesian clustering methods have recently been used for the integrated analysis of multi-omics data, no method designed to analyze multi-omics microbiome data has been proposed. In this study, we propose a novel framework called integrative stochastic variational variable selection (I-SVVS), which is an extension of stochastic variational variable selection for high-dimensional microbiome data. The I-SVVS approach addresses a specific Bayesian mixture model for each type of omics data, such as an infinite Dirichlet multinomial mixture model for microbiome data and an infinite Gaussian mixture model for metabolomic data. This approach is expected to reduce the computational time of the clustering process and improve the accuracy of the clustering results. Additionally, I-SVVS identifies a critical set of representative variables in multi-omics microbiome data. Three datasets from soybean, mice, and humans (each set integrated microbiome and metabolome) were used to demonstrate the potential of I-SVVS. The results indicate that I-SVVS achieved improved accuracy and faster computation compared to existing methods across all test datasets. It effectively identified key microbiome species and metabolites characterizing each cluster. For instance, the computational analysis of soybean dataset, including 377 samples with 16,943 microbiome species and 265 metabolome features, was completed in 2.18 hours using I-SVVS, compared to 2.35 days with Clusternomics and 1.12 days with iClusterPlus. The software for this analysis, written in Python, is freely available at https://github.com/tungtokyo1108/I-SVVS.

bioinformatics↗

bioRxiv2022

The forward variable selection based optimized decoding toolbox for neuroimaging data

Neuroimaging data is complex and high-dimensional that poses challenges for machine learning (ML) applications. Of varieties of reasons contributing on accuracy decoding, variable feature selection is one of crucial steps for determining target feature in data analysis, especially in the context of neuroimaging studies where the number of features is often much larger than the number of observations. Therefore, optimization of feature selection from such high-dimensional neuroimaging data has been challenging using conventional ML algorithms. Here, we introduce an efficient ML package incorporating a forward variable selection (FVS) algorithm that optimizes the identification of features for both classification and regression models. In our framework, the best ML model and feature pairs that explain the inputs can be automatically determined. Moreover, the toolbox can be executed in a parallel environment for efficient computation. The parallelized FVS algorithm iteratively selects the best feature pair compared against the previous steps to maximize the predictive performance. The FVS algorithm evaluates the goodness-of-fit across different models using the k-fold cross validation and identifies the best subset of features based on a pre-defined criterion for each model. Furthermore, the hyperparameters of each ML model are optimized at each forward iteration. A final outcome highlights an optimized number of selected features (brain regions of interest) with decoding accuracies. Using our pipeline, we examined the effectiveness of our toolbox on an existing neuroimaging (structural MRI) dataset. Compared ML models with and without FVS approach, we demonstrate that the FVS significantly improved the accuracy of the ML algorithm over the counterpart model without FVS. Furthermore, we confirmed the use of parallel computation considerably reduced the computational burden for the high-dimensional MRI data. This oFVSD toolbox efficiently and effectively improves the performance of both classification and regression models on neuroimaging data and should be applicable to many other neuroimaging data and more. This Python package is open-source and freely available, making it a useful toolbox for neuroimaging communities seeking improvement of decoding accuracy for their datasets.

neuroscience↗

bioRxiv2022

Directed Biosynthesis of Mitragynine Stereoisomers

Mitragyna speciosa ("Kratom") is used as a natural remedy for pain and management of opioid dependence. The pharmacological properties of Kratom have been linked to a complex mixture of monoterpene indole alkaloids, most notably mitragynine. Here, we report the central biosynthetic steps responsible for the scaffold formation of mitragynine and related corynanthe-type alkaloids. We illuminate the mechanistic basis by which the key stereogenic centre of this scaffold is formed. These discoveries were leveraged for the enzymatic production of mitragynine, the C-20 epimer speciogynine, and a series of fluorinated analogues.

biochemistry↗

bioRxiv2022

Enriching Biomedical Knowledge for Low-resource Language Through Translation

Biomedical data and benchmarks are highly valuable yet very limited in low-resource languages other than English such as Vietnamese. In this paper, we make use of a state-of-theart translation model in English-Vietnamese to translate and produce both pretrained as well as supervised data in the biomedical domains. Thanks to such large-scale translation, we introduce ViPubmedT5, a pretrained Encoder-Decoder Transformer model trained on 20 million translated abstracts from the high-quality public PubMed corpus. ViPubMedT5 demonstrates state-of-the-art results on two different biomedical benchmarks in summarization and acronym disambiguation. Further, we release ViMedNLI a new NLP task in Vietnamese translated from MedNLI using the recently public En-vi translation model and carefully refined by human experts, with evaluations of existing methods against ViPubmedT5.

bioinformatics↗

bioRxiv2022

Length-limitation of astral microtubules orients cell divisions in intestinal crypts

Planar spindle orientation is critical for epithelial tissue organization, and generally instructed from the long cell shape axis or cortical polarity domains. We introduced mouse intestinal organoid crypts to study spindle orientation in a monolayered mammalian epithelium. Although spindles were planar in this tissue, mitotic cells remained elongated along the apico-basal axis and polarity complexes were segregated to basal poles, so that spindles oriented in an unconventional manner, orthogonal to both polarity and geometric cues. Using high-resolution 3D imaging, simulations, cell shape and cytoskeleton manipulations, we show that planar divisions resulted from a length-limitation in mitotic-phase astral microtubules which precludes them from interacting with basal polarity, and oriented spindles from the local geometry of apical domains. Accordingly, lengthening microtubules affected spindle planarity, cell positioning and crypt arrangement. We conclude that microtubule length regulation may serve as a key mechanism for spindles to sense local cell shapes and tissue forces to preserve mammalian epithelial architecture.

developmental biology↗

bioRxiv2022

The Arabidopsis INNER NO OUTER (INO) gene acts exclusively and quantitatively in regulation of ovule outer integument development

The INNER NO OUTER (INO) gene is essential for formation of the outer integument of ovules in Arabidopsis thaliana. Initially described lesions in INO were missense mutations resulting in aberrant mRNA splicing. To determine the null mutant phenotype we used CRISPR to induce frame-shift mutations and found, in confirmation of results on another recently identified frame-shift mutation (Vijayan et al., 2021), that such mutants have a phenotype identical to the most severe splicing mutant (ino-1), with effects specific to outer integument development. We show that the altered protein of an ino mRNA splicing mutant with a less severe phenotype (ino-4) does not have INO activity, and the mutant is partial because it produces a small amount of correctly spliced INO mRNA. Screening for suppressors of ino-4 in a fast-neutron mutagenized population identified a genetic duplication of the ino-4 gene leading to an increase in the amount of this mRNA. The increased expression led to a decrease in the severity of the mutant effects, indicating that the amount of INO activity quantitatively regulates outer integument growth. The results further confirm that the role of INO in Arabidopsis development is specific to the outer integument of ovules where it quantitatively affects the growth of this structure.

plant biology↗